Abstract This paper presents a novel approach to automated plant disease detection and classification using advanced image processing and deep learning techniques. Early detection of plant diseases is crucial for sustainable agricultural practices and food security. Our proposed system leverages convolutional neural networks (CNNs) to analyze leaf images and accurately identify various plant diseases across multiple crop species. The methodology includes image preprocessing, segmentation, feature extraction, and classification using a custom CNN architecture. The system was trained and validated on a diverse dataset containing 38,000 images spanning 14 crop species and 26 diseases. Experimental results demonstrate 97.89% classification accuracy, outperforming existing methods. The system is implemented as a lightweight mobile application allowing farmers to diagnose plant diseases in real-time using only a smartphone camera, potentially reducing crop losses and pesticide usage through early intervention. This research contributes to precision agriculture by providing an accessible, cost-effective tool for disease management in both developed and developing agricultural contexts.
Ramu Vankudoth (Wed,) studied this question.